The effect of gene flow from unsampled demes in landscape genetic analysis

Andrew J. Shirk, Erin L. Landguth, Samuel A. Cushman

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


An assumption of correlative landscape genetic methods is that genetic differentiation at neutral markers arises solely from the degree to which the intervening landscape between individuals or populations resists gene flow. However, this assumption is violated when gene flow occurs into the sampled population from an unsampled, differentiated deme. This may happen when sampling within only a portion of a population's extent or when closely related species hybridize with the sampled population. In both cases, violation of the modelling assumptions has the potential to reduce landscape genetic model selection accuracy and result in poor inferences. We used individual-based population genetic simulations in complex landscapes within a model selection framework to explore the potential confounding effect of gene flow from unsampled demes. We hypothesized that as gene flow from outside the sampling extent increased, model selection accuracy would decrease due to the formation of a hybrid zone where allele frequencies were perturbed in a way that was not correlated with effective distances between sampled individuals. Surprisingly, we found this expectation was unfounded, because the reduced accuracy due to admixture was counteracted by an increase in allelic diversity as alleles spread from the unsampled deme into the sampled population. These new alleles increased the power to detect landscape genetic relationships and even slightly improving model selection accuracy overall. This is a reassuring result, suggesting that sampling the full extent of a population or related species that may hybridize may be unnecessary, as long as other well-established sampling requirements are met.

Original languageEnglish
Pages (from-to)394-403
Number of pages10
JournalMolecular Ecology Resources
Issue number2
StatePublished - Feb 2021


  • accuracy
  • deme
  • gene flow
  • landscape genetics
  • model selection
  • sampling


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